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Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN

Kenneth Chan1, Elizabeth Wahome2, Apostolos Tsiachristas3

  • 1Acute Multidisciplinary Imaging and Interventional Centre, British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK.

Lancet (London, England)
|June 1, 2024
PubMed

Insights

The perivascular fat attenuation index (FAI) Score measures coronary inflammation and predicts cardiac events, especially in patients without obstructive coronary artery disease (CAD). An AI-Risk algorithm integrating FAI Score improves cardiovascular risk prediction beyond traditional methods.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Coronary computed tomography angiography (CCTA) is a primary tool for chest pain assessment and guiding revascularization.
  • A significant number of patients without obstructive coronary artery disease (CAD) identified via CCTA have an unclear prognosis and management pathway.
  • Measuring coronary inflammation using the perivascular fat attenuation index (FAI) Score from CCTA may enhance cardiovascular risk prediction and guide management in these individuals.

Purpose of the Study:

  • To evaluate the risk profile and event rates in patients undergoing CCTA within the UK National Health Service (NHS).
  • To test the hypothesis that coronary arterial inflammation, assessed by FAI Score, contributes to cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD.
  • To externally validate an artificial intelligence (AI)-Risk prognostic algorithm and its classification system in a UK population.

Main Methods:

  • A multicentre, longitudinal cohort study of 40,091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals.
  • Follow-up for MACE (myocardial infarction, new heart failure, or cardiac death) for a median of 2.7 years.
  • Evaluation of FAI Score's prognostic value in 3,393 patients with longer follow-up, and assessment of an AI-enhanced risk prediction algorithm integrating FAI Score, plaque metrics, and clinical factors.

Main Results:

  • In patients without obstructive CAD (81.1%), MACE and cardiac deaths were substantial.
  • Increased FAI Score in all three coronary arteries significantly increased the risk of cardiac mortality (HR 29.8) and MACE (HR 12.6).
  • FAI Score independently predicted cardiac mortality and MACE, and the AI-Risk classification was strongly associated with adverse outcomes.

Conclusions:

  • The FAI Score quantifies inflammatory risk beyond current clinical stratification and CCTA interpretation, particularly in patients without obstructive CAD.
  • The AI-Risk algorithm, incorporating FAI Score, offers a potential alternative to traditional risk calculators for prognostic assessment.
  • This approach could improve the management and risk stratification of patients undergoing CCTA.
Abstract

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